Towards automated classification of clinical optical coherence tomography data of dense tissues.
The native contrast of optical coherence tomography (OCT) data in dense tissues can pose a challenge for clinical decision making. Automated data evaluation is one way of enhancing the clinical utility of measurements. Methods for extracting information from structural OCT data are appraised here. A-scan analysis allows characterization of layer thickness and scattering parameters, whereas image analysis renders itself to segmentation, texture and speckle analysis. All fully automated approaches combine pre-processing, feature registration, data reduction, and classification. Pre-processing requires de-noising, feature recognition, normalization and refining. In the current literature, image exclusion criteria, initial parameters, or manual input are common requirements. The interest of the presented methods lies in the prospect of objective, quick, and/or post-acquisition processing. There is a potential to improve clinical decision making based on automated processing of OCT data.
Bazant-Hegemark
Florian
F
Cranfield Health, Cranfield University at Silsoe, Silsoe, Bedfordshire, UK.
Stone
Nicholas
N
D000818:Animals; D050197:Atherosclerosis / Q000175:diagnosis; D001331:Automation; D001786:Blood Glucose / Q000032:analysis; D001921:Brain / Q000033:anatomy & histology; D003627:Data Interpretation, Statistical; D005123:Eye / Q000033:anatomy & histology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008264:Macrophages / Q000473:pathology; D009375:Neoplasms, Glandular and Epithelial / Q000175:diagnosis; D055620:Optical Phenomena; D012031:Refractometry / Q000706:statistics & numerical data; D041623:Tomography, Optical Coherence / Q000706:statistics & numerical data*
D001786:Blood Glucose